{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MXNet for Collaborative Deep Learning in Recommender Systems\n",
    "In this tutorial, we build on MXNet to implement the Collaborative Deep Learning (CDL) [1] model for recommender systems.\n",
    "\n",
    "## Brief Introduction of CDL\n",
    "\n",
    "In CDL, a probabilistic stacked denoising autoencoder (pSDAE) is connected to a matrix factorization (MF) component. Model training will alternate between pSDAE and MF. In each epoch, a pSDAE with a reconstruction target at the end and a regression target in the bottleneck will be udpated before updating the latent factors U and V in the regularized MF.\n",
    "\n",
    "Below is the graphical model for CDL. The part in the red rectangle is pSDAE and the rest is the MF component regularized by pSDAE. Essentially, the updating will alternate between pSDAE (updating $W^+$) and the MF component (updating $u$ and $v$).\n",
    "### Some Notation:\n",
    "- $x_0$: the input vectors to pSDAE (corrupted data, e.g., randomly deleting some entries of the input)\n",
    "- $x_c$: the reconstruction target vectors (the uncorrupted data)\n",
    "- $x_{L/2}$: the output vectors of pSDAE's middle layer (bottleneck layer)\n",
    "- $X_0$: the matrix consists of vectors $x_0$\n",
    "- $X_c$: the matrix consists of vectors $x_c$\n",
    "- $W^+$: weights and biases of pSDAE\n",
    "- $v$: latent item vectors\n",
    "- $u$: latent user vectors\n",
    "- $R$: rating matrix ('1' if the article is in the user's library and '0' otherwise)\n",
    "- $I$: number of users\n",
    "- $J$: number of items\n",
    "- $\\lambda_u$, $\\lambda_v$, $\\lambda_w$, $\\lambda_n$: hyperparameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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zqQUnto/u92mzoZ7FTNORE6PUAj2GCz73NRg1beroYROODDpwxVcZAGJjY8vL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\nBEGDEEzwEARBEDQI/R8hMmyLVFiaQQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image \n",
    "Image(filename='PGM-CDL.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below we show a special case of CDL (from a neural network perspective), where it degenerates to simultaneously training two neural networks overlaid together with a common input layer (the corrupted input) but different output layers. This might be a lot easier to understand for people not familiar with graphical models. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ffvjw4ceOHSst+dnZ2cePHx8xYkT79u29vLyio6NNaaj7htKyZcuYmJjatWu3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whzXwCZYT3NRd5XEKRCY5pvhpM510IuNQA41kIm1io8e/W4K1jc06AX5ZUVll0L/FlwcWuHT+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\nb+8CMID0AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(filename='NN-CDL.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The objective function (which we use in this implementation) for this special case is:\n",
    "$$\n",
    "\\begin{align}\n",
    "\\mathscr{L}=&-\\frac{\\lambda_u}{2}\\sum_i ||u_i||_2^2-\\frac{\\lambda_w}{2}\\sum_l(||W_l\\|_F^2+\\|b_l||_2^2) \\nonumber \\\\\n",
    "&-\\frac{\\lambda_v}{2}\\sum_j||v_j-f_e(X_{0,j*},W^+)^T||_2^2 \\nonumber \\\\\n",
    "&-\\frac{\\lambda_n}{2}\\sum_j||f_r(X_{0,j*},W^+)-X_{c,j*}||_2^2 \\nonumber \\\\\n",
    "&-\\sum_{i,j}\\frac{C_{ij}}{2}(R_{ij}-u_i^Tv_j)^2,\\nonumber \\\\\n",
    "\\end{align}\n",
    "$$\n",
    "where the encoder function $f_e(\\cdot,W^+)$ takes the corrupted content vector $X_{0,j*}$ of item $j$ as input and computes the encoding of the item, and the function $f_r(\\cdot,W^+)$ also takes $X_{0,j*}$ as input, computes the encoding and then the reconstructed content vector of item $j$. Here $\\lambda_w$, $\\lambda_n$, $\\lambda_u$, and $\\lambda_v$ are hyperparameters and $C_{ij}$ is a confidence parameter ($C_{ij} = a$ if $R_{ij}=1$ and $C_{ij}=b$ otherwise). For example, if the number of layers $L=6$, $f_e(X_{0,j*},W^+)$ is the output of the third layer while $f_r(X_{0,j*},W^+)$ is the output of the sixth layer.\n",
    "\n",
    "To learn CDL, we have to implement the block coordinate descent (BCD) update using numpy/mshadow and call this BCD procedure after each epoch of pSDAE. Besides the MF part, another difference between CDL and conventional deep learning models is that pSDAE has a fixed target at the end and a dynamic target (the latent item factors V) in the bottleneck layer. It might need some hacking to make this work.\n",
    "\n",
    "[1] H. Wang, N. Wang, and D. Yeung. Collaborative deep learning for recommender systems. In KDD, 2015."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Implementing CDL in MXNet for Recommender Systerms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import mxnet as mx\n",
    "import numpy as np\n",
    "import logging\n",
    "import data\n",
    "from math import sqrt\n",
    "from autoencoder import AutoEncoderModel\n",
    "import os"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setting Hyperparameters\n",
    "- lambda_u: regularization coefficent for user latent matrix U\n",
    "- lambda_v: regularization coefficent for item latent matrix V\n",
    "- K: number of latent factors\n",
    "- is_dummy: whether to use a dummy dataset for demo\n",
    "- num_iter: number of iterations (minibatches) to train (a epoch in the used dataset takes about 68 iterations)\n",
    "- batch_size: minibatch size\n",
    "- dir_save: directory to save training results\n",
    "- lv: lambda_v/lambda_n in CDL; this controls the trade-off between reconstruction error in pSDAE and recommendation accuracy during training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lambda_u = 1 # lambda_u in CDL\n",
    "lambda_v = 10 # lambda_v in CDL\n",
    "K = 50\n",
    "p = 1\n",
    "is_dummy = False\n",
    "num_iter = 100 # about 68 iterations/epoch, the recommendation results at the end need 100 epochs\n",
    "batch_size = 256\n",
    "\n",
    "np.random.seed(1234) # set seed\n",
    "lv = 1e-2 # lambda_v/lambda_n in CDL\n",
    "dir_save = 'cdl%d' % p"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create the directory and the log file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p1: lambda_v/lambda_u/ratio/K: 10.000000/1.000000/0.010000/50\n"
     ]
    }
   ],
   "source": [
    "if not os.path.isdir(dir_save):\n",
    "    os.system('mkdir %s' % dir_save)\n",
    "fp = open(dir_save+'/cdl.log','w')\n",
    "print 'p%d: lambda_v/lambda_u/ratio/K: %f/%f/%f/%d' % (p,lambda_v,lambda_u,lv,K)\n",
    "fp.write('p%d: lambda_v/lambda_u/ratio/K: %f/%f/%f/%d\\n' % \\\n",
    "        (p,lambda_v,lambda_u,lv,K))\n",
    "fp.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading Data\n",
    "Here we load the text information (as input to pSDAE) in the file mult.dat and the rating matrix (as input for the MF part) in the file cf-train-1-users.dat. Code for loading the data are packed in data.py.\n",
    "\n",
    "We use the CiteULike dataset here. The input text is bag-of-words vectors normalized to [0,1]. Some details:\n",
    "- task: recommend articles to users\n",
    "- number of users: 5551\n",
    "- number of items: 16980\n",
    "- number of ratings for training: ~169800\n",
    "- number of terms: 8000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "if is_dummy:\n",
    "    X = data.get_dummy_mult()\n",
    "    R = data.read_dummy_user()\n",
    "else:\n",
    "    X = data.get_mult()\n",
    "    R = data.read_user()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Network Definition\n",
    "Here we deine the logging level and construct the network. As mentioned before, pSDAE has multiple targets, which is why we have to group them as one single symbol (see the commented code below)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "logging.basicConfig(level=logging.INFO)\n",
    "cdl_model = AutoEncoderModel(mx.cpu(2), [X.shape[1],100,K],\n",
    "    pt_dropout=0.2, internal_act='relu', output_act='relu')\n",
    "'''\n",
    "We use the following code to define the pSDAE stucture mentioned before. fe_loss is the regression loss for the bottleneck layer,\n",
    "and fr_loss is the reconstruction loss in the last layer.\n",
    "\n",
    "            fe_loss = mx.symbol.LinearRegressionOutput(data=self.lambda_v_rt*self.encoder,\n",
    "                label=self.lambda_v_rt*self.V)\n",
    "            fr_loss = mx.symbol.LinearRegressionOutput(data=self.decoder, label=self.data)\n",
    "            self.loss = mx.symbol.Group([fe_loss, fr_loss])            \n",
    "'''"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Initializing Variables\n",
    "Here we initialize several variables. V is the latent item matrix and lambda_v_rt is an ndarray with entries equal to sqrt(lv). We need this lambda_v_rt to hack the trade-off between two targets in pSDAE."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_X = X\n",
    "V = np.random.rand(train_X.shape[0],K)/10\n",
    "lambda_v_rt = np.ones((train_X.shape[0],K))*sqrt(lv)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training the CDL\n",
    "Train the whole CDL (joint training of pSDAE and the connected MF). We use SGD for pSDAE and BCD for the MF part. U is the user latent matrix, V is the item latent matrix, theta is the output of pSDAE's middle layer, and BCD_loss equals to rating_loss+reg_loss_for_U+reg_loss_for_V. For demostration we train for only 100 iterations (about 1.5 epochs) here. The shown recommendations in later parts are results after 100 epochs.\n",
    "\n",
    "For more details, see the commented code below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:Fine tuning...\n",
      "INFO:root:Iter:0 metric:0.001668\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1 - tr_err/bcd_err/rec_err: 53641376.1/27755.0/53613621.1\n",
      "Training ends.\n"
     ]
    }
   ],
   "source": [
    "U, V, theta, BCD_loss = cdl_model.finetune(train_X, R, V, lambda_v_rt, lambda_u,\n",
    "        lambda_v, dir_save, batch_size,\n",
    "        num_iter, 'sgd', l_rate=0.1, decay=0.0,\n",
    "        lr_scheduler=mx.misc.FactorScheduler(20000,0.1))\n",
    "print 'Training ends.'\n",
    "'''\n",
    "The function finetune above will call the function 'solve' in the solver.py, where the customized training loop resides. \n",
    "In the training loop, we call the following code after each epoch of pSDAE to update U and V using BCD. The BCD updating\n",
    "procedure is wrapped up in the function BCD_one. Note that after each epoch, we upate U and V for only one iteration.\n",
    "                theta = model.extract_feature(sym[0], args, auxs,\n",
    "                    data_iter, X.shape[0], xpu).values()[0]\n",
    "                # update U, V and get BCD loss\n",
    "                U, V, BCD_loss = BCD_one(R, U, V, theta,\n",
    "                    lambda_u, lambda_v, dir_save, True)\n",
    "                # get recon' loss\n",
    "                Y = model.extract_feature(sym[1], args, auxs,\n",
    "                    data_iter, X.shape[0], xpu).values()[0]\n",
    "                Recon_loss = lambda_v/np.square(lambda_v_rt_old[0,0])*np.sum(np.square(Y-X))/2.0\n",
    "                lambda_v_rt[:] = lambda_v_rt_old[:] # back to normal lambda_v_rt\n",
    "                data_iter = mx.io.NDArrayIter({'data': X, 'V': V, 'lambda_v_rt':\n",
    "                    lambda_v_rt},\n",
    "                    batch_size=batch_size, shuffle=False,\n",
    "                    last_batch_handle='pad')\n",
    "                data_iter.reset()\n",
    "                batch = data_iter.next()\n",
    "'''"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Saving Models and Parameters\n",
    "Save the network (pSDAE) parameters, latent matrices, and middle-layer output."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cdl_model.save(dir_save+'/cdl_pt.arg')\n",
    "np.savetxt(dir_save+'/final-U.dat.demo',U,fmt='%.5f',comments='')\n",
    "np.savetxt(dir_save+'/final-V.dat.demo',V,fmt='%.5f',comments='')\n",
    "np.savetxt(dir_save+'/final-theta.dat.demo',theta,fmt='%.5f',comments='')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Computing Training Error\n",
    "The training loss consists of the loss in pSDAE and that in MF."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training error: 53629559.864\n"
     ]
    }
   ],
   "source": [
    "Recon_loss = lambda_v/lv*cdl_model.eval(train_X,V,lambda_v_rt)\n",
    "print \"Training error: %.3f\" % (BCD_loss+Recon_loss)\n",
    "fp = open(dir_save+'/cdl.log','a')\n",
    "fp.write(\"Training error: %.3f\\n\" % (BCD_loss+Recon_loss))\n",
    "fp.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generating Recommendations\n",
    "Load the latent matrices (U and V), compute the predicted ratings R=UV^T, and generate recommendation lists for each user. There 5551 users in the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "User 100\n",
      "User 200\n",
      "User 300\n",
      "User 400\n",
      "User 500\n",
      "User 600\n",
      "User 700\n",
      "User 800\n",
      "User 900\n",
      "User 1000\n",
      "User 1100\n",
      "User 1200\n",
      "User 1300\n",
      "User 1400\n",
      "User 1500\n",
      "User 1600\n",
      "User 1700\n",
      "User 1800\n",
      "User 1900\n",
      "User 2000\n",
      "User 2100\n",
      "User 2200\n",
      "User 2300\n",
      "User 2400\n",
      "User 2500\n",
      "User 2600\n",
      "User 2700\n",
      "User 2800\n",
      "User 2900\n",
      "User 3000\n",
      "User 3100\n",
      "User 3200\n",
      "User 3300\n",
      "User 3400\n",
      "User 3500\n",
      "User 3600\n",
      "User 3700\n",
      "User 3800\n",
      "User 3900\n",
      "User 4000\n",
      "User 4100\n",
      "User 4200\n",
      "User 4300\n",
      "User 4400\n",
      "User 4500\n",
      "User 4600\n",
      "User 4700\n",
      "User 4800\n",
      "User 4900\n",
      "User 5000\n",
      "User 5100\n",
      "User 5200\n",
      "User 5300\n",
      "User 5400\n",
      "User 5500\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from data import read_user\n",
    "def cal_rec(p,cut):\n",
    "    R_true = read_user('cf-test-1-users.dat')\n",
    "    dir_save = 'cdl'+str(p)\n",
    "    U = np.mat(np.loadtxt(dir_save+'/final-U.dat'))\n",
    "    V = np.mat(np.loadtxt(dir_save+'/final-V.dat'))\n",
    "    R = U*V.T\n",
    "    num_u = R.shape[0]\n",
    "    num_hit = 0\n",
    "    fp = open(dir_save+'/rec-list.dat','w')\n",
    "    for i in range(num_u):\n",
    "        if i!=0 and i%100==0:\n",
    "            print 'User '+str(i)\n",
    "        l_score = R[i,:].A1.tolist()\n",
    "        pl = sorted(enumerate(l_score),key=lambda d:d[1],reverse=True)\n",
    "        l_rec = list(zip(*pl)[0])[:cut]\n",
    "        s_rec = set(l_rec)\n",
    "        s_true = set(np.where(R_true[i,:]>0)[1].A1)\n",
    "        cnt_hit = len(s_rec.intersection(s_true))\n",
    "        fp.write('%d:' % cnt_hit)\n",
    "        fp.write(' '.join(map(str,l_rec)))\n",
    "        fp.write('\\n')\n",
    "    fp.close()\n",
    "\n",
    "cal_rec(1,8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Show Recommendations\n",
    "Load the article titles (raw-data.csv), ratings (cf-train-1-users.dat and cf-test-1-users.dat), and recommendation lists (rec-list.dat)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import csv\n",
    "from data import read_user\n",
    "import numpy as np\n",
    "p = 1\n",
    "# read predicted results\n",
    "dir_save = 'cdl%d' % p\n",
    "csvReader = csv.reader(open('raw-data.csv','rb'))\n",
    "d_id_title = dict()\n",
    "for i,row in enumerate(csvReader):\n",
    "    if i==0:\n",
    "        continue\n",
    "    d_id_title[i-1] = row[3]\n",
    "R_test = read_user('cf-test-1-users.dat')\n",
    "R_train = read_user('cf-train-1-users.dat')\n",
    "fp = open(dir_save+'/rec-list.dat')\n",
    "lines = fp.readlines()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Show the titles of articles in the training set and titles of recommended articles. Correctly recommended articles are marked by asterisks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "##########  User 3  ##########\n",
      "\n",
      "#####  Articles in the Training Sets  #####\n",
      "Formal Ontology and Information Systems\n",
      "Ontologies: a silver bullet for knowledge management and electronic commerce\n",
      "Business Process Execution Language for Web Services version 1.1\n",
      "Unraveling the Web services web: an introduction to SOAP, WSDL, and UDDI\n",
      "A cookbook for using the model-view controller user interface paradigm in Smalltalk-80\n",
      "Object-oriented application frameworks\n",
      "Data integration: a theoretical perspective\n",
      "Web services: been there, done that?\n",
      "Sweetening Ontologies with DOLCE\n",
      "Naive Geography\n",
      "\n",
      "#####  Articles Recommended (Correct Ones Marked by Asterisks)  #####\n",
      "* The Semantic Web\n",
      "* A Translation Approach to Portable Ontology Specifications\n",
      "The Semantic Web Revisited\n",
      "Towards principles for the design of ontologies used for knowledge sharing\n",
      "Semantic integration: a survey of ontology-based approaches\n",
      "OWL Web Ontology Language Overview\n",
      "Toward Principles for the Design of Ontologies Used for Knowledge Sharing\n",
      "A survey of approaches to automatic schema matching\n"
     ]
    }
   ],
   "source": [
    "user_id = 3\n",
    "s_test = set(np.where(R_test[user_id,:]>0)[1].A1)\n",
    "l_train = np.where(R_train[user_id,:]>0)[1].A1.tolist()\n",
    "l_pred = map(int,lines[user_id].strip().split(':')[1].split(' '))\n",
    "print '##########  User '+str(user_id)+'  ##########\\n'\n",
    "print '#####  Articles in the Training Sets  #####'\n",
    "for i in l_train:\n",
    "    print d_id_title[i]\n",
    "print '\\n#####  Articles Recommended (Correct Ones Marked by Asterisks)  #####'\n",
    "for i in l_pred:\n",
    "    if i in s_test:\n",
    "        print '* '+d_id_title[i]\n",
    "    else:\n",
    "        print d_id_title[i]\n",
    "fp.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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